Abstract
Data envelopment analysis (DEA) model selection is problematic. The estimated efficiency for any DMU depends on the inputs and outputs included in the model. It also depends on the number of outputs plus inputs. It is clearly important to select parsimonious specifications and to avoid as far as possible models that assign full high-efficiency ratings to DMUs that operate in unusual ways (mavericks). A new method for model selection is proposed in this paper. Efficiencies are calculated for all possible DEA model specifications. The results are analysed using Principal Component Analysis. It is shown that model equivalence or dissimilarity can be easily assessed using this approach. The reasons why particular DMUs achieve a certain level of efficiency with a given model specification become clear. The methodology has the additional advantage of producing DMU rankings. These rankings can always be established independently of whether the model is estimated under constant or under variable returns to scale.
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Acknowledgements
We are grateful to an anonymous referee for pointing out that the method proposed here overcomes the CRS limitation on super-efficiency approaches. The participation of Cecilio Mar Molinero in this project was possible thanks to a Ramóu y Cajal grant from The Spanish Ministry of Science and Technology.
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Cinca, C., Molinero, C. Selecting DEA specifications and ranking units via PCA. J Oper Res Soc 55, 521–528 (2004). https://doi.org/10.1057/palgrave.jors.2601705
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DOI: https://doi.org/10.1057/palgrave.jors.2601705